SoroushVahidi/scidocs huggingface dataset:
The many faces of publish/subscribe
(2003)

Well adapted to the loosely coupled nature of distributed interaction in large-scale applications, the publish/subscribe communication paradigm has recently received increasing attention. With systems based on the publish/subscribe interaction scheme, subscribers register their interest in an event, or a pattern of events, and are subsequently asynchronously notified of events generated by publishers. Many variants of the paradigm have recently been proposed, each variant being specifically adapted to some given application or network model. This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many variations on the theme of publish/subscribe are classified and synthesized. In particular, their respective benefits and shortcomings are discussed both in terms of interfaces and implementations.
A 37.5 /spl mu/W Body Channel Communication Wake-Up Receiver With Injection-Locking Ring Oscillator for Wireless Body Area Network
(2013)

An ultra-low power wake-up receiver for body channel communication (BCC) is implemented in 130 nm CMOS process. The proposed wake-up receiver uses the injection-locking ring oscillator (ILRO) to replace the RF amplifier with low power consumption. Through the ILRO, the frequency modulated input signal is amplified to the full swing rectangular signal directly demodulated by the following low power PLL-based FSK demodulator. In addition, the energy-efficient BCC link mitigates the sensitivity and selectivity requirements for the receiver, which significantly reduces the power consumption. Furthermore, the auto frequency calibrator (AFC) is adopted to compensate the free running frequency of the ring oscillator which is caused by temperature variation and leakage current. The AFC reuses the PLL-based demodulator to periodically set the free running frequency to the desired frequency without any area overhead. As a result, the proposed wake-up receiver achieves a sensitivity of -62.7 dBm at a data rate of 200 kbps while consuming only 37.5 μW from the 0.7 V supply.
2D-to-3D image conversion by learning depth from examples
(2012)

Among 2D-to-3D image conversion methods, those involving human operators have been most successful but also time-consuming and costly. Automatic methods, that typically make use of a deterministic 3D scene model, have not yet achieved the same level of quality as they often rely on assumptions that are easily violated in practice. In this paper, we adopt the radically different approach of “learning” the 3D scene structure. We develop a simplified and computationally-efficient version of our recent 2D-to-3D image conversion algorithm. Given a repository of 3D images, either as stereopairs or image+depth pairs, we find k pairs whose photometric content most closely matches that of a 2D query to be converted. Then, we fuse the k corresponding depth fields and align the fused depth with the 2D query. Unlike in our original work, we validate the simplified algorithm quantitatively on a Kinect-captured image+depth dataset against the Make3D algorithm. While far from perfect, the presented results demonstrate that online repositories of 3D content can be used for effective 2D-to-3D image conversion.
User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop
(2010)

Collaborative Filtering(CF) algorithms are widely used in a lot of recommender systems, however, the computational complexity of CF is high thus hinder their use in large scale systems. In this paper, we implement user-based CF algorithm on a cloud computing platform, namely Hadoop, to solve the scalability problem of CF. Experimental results show that a simple method that partition users into groups according to two basic principles, i.e., tidy arrangement of mapper number to overcome the initiation of mapper and partition task equally such that all processors finish task at the same time, can achieve linear speedup.
Corn Disease Identification from Leaf Images Using Convolutional Neural Networks
(2017)

Plant diseases are one of the major factors that can negatively impact crop yields. Therefore, we want to create an automated system that can identify plant diseases as easily as possible. Our research goal is to help crop growers who might not be experts in plant pathology be able to identify diseases as soon as they occur in order to minimize the spread of disease and the damage to the crops. We propose a very light-weight convolutional neural network architecture that is trained to classify images of corn leaves into one of the four categories: common rust disease, northern leaf blight disease, gray leaf spot disease, or healthy. The dataset used is the PlantVillage dataset taken from plantvillage.org. The dataset consists of 8506 images of corn leaves from 4 categories. We evaluated our model using 5-fold cross-validation, and we achieves an accuracy of 97.09 % on average. The result of our model is slightly worse than the performance of other well-known CNNs architectures, but the complexity of our architecture is much lower, making it more suitable for real-time inferences on resource-constrained devices such as smartphones.
Argument discovery via crowdsourcing
(2017)

The amount of controversial issues being discussed on the Web has been growing dramatically. In articles, blogs, and wikis, people express their points of view in the form of arguments, i.e., claims that are supported by evidence. Discovery of arguments has a large potential for informing decision-making. However, argument discovery is hindered by the sheer amount of available Web data and its unstructured, free-text representation. The former calls for automatic text-mining approaches, whereas the latter implies a need for manual processing to extract the structure of arguments. In this paper, we propose a crowdsourcing-based approach to build a corpus of arguments, an argumentation base, thereby mediating the trade-off of automatic text-mining and manual processing in argument discovery. We develop an end-to-end process that minimizes the crowd cost while maximizing the quality of crowd answers by: (1) ranking argumentative texts, (2) pro-actively eliciting user input to extract arguments from these texts, and (3) aggregating heterogeneous crowd answers. Our experiments with real-world datasets highlight that our method discovers virtually all arguments in documents when processing only 25% of the text with more than 80% precision, using only 50% of the budget consumed by a baseline algorithm.
Comparative study of automatic speech recognition techniques
(2013)

Over the past decades, extensive research has been carried out on various possible implementations of automatic speech recognition (ASR) systems. The most renowned algorithms in the field of ASR are the mel-frequency cepstral coefficients and the hidden Markov models. However, there are also other methods, such as wavelet-based transforms, artificial neural networks and support vector machines, which are becoming more popular. This review article presents a comparative study on different approaches that were proposed for the task of ASR, and which are widely used nowadays.
Computational approaches to motor control
(1997)

This review will focus on four areas of motor control which have recently been enriched both by neural network and control system models: motor planning, motor prediction, state estimation and motor learning. We will review the computational foundations of each of these concepts and present specific models which have been tested by psychophysical experiments. We will cover the topics of optimal control for motor planning, forward models for motor prediction, observer models of state estimation arid modular decomposition in motor learning. The aim of this review is to demonstrate how computational approaches, as well as proposing specific models, provide a theoretical framework to formalize the issues in motor control.
Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning
(2016)

We introduce a framework for learning regrasping behaviors based on tactile data. First, we present a grasp stability predictor that uses spatio-temporal tactile features collected from the early-object-lifting phase to predict the grasp outcome with a high accuracy. Next, the trained predictor is used to supervise and provide feedback to a reinforcement learning algorithm that learns the required grasp adjustments based on tactile feedback. Our results gathered over more than 50 hours of real robot experiments indicate that the robot is able to predict the grasp outcome with 93% accuracy. In addition, the robot is able to improve the grasp success rate from 42% when randomly grasping an object to up to 97% when allowed to regrasp the object in case of a predicted failure.
Cross-Modal Multistep Fusion Network With Co-Attention for Visual Question Answering
(2018)

Visual question answering (VQA) is receiving increasing attention from researchers in both the computer vision and natural language processing fields. There are two key components in the VQA task: feature extraction and multi-modal fusion. For feature extraction, we introduce a novel co-attention scheme by combining Sentence-guide Word Attention (SWA) and Question-guide Image Attention in a unified framework. To be specific, the textual attention SWA relies on the semantics of the whole question sentence to calculate contributions of different question words for text representation. For the multi-modal fusion, we propose a “Cross-modal Multistep Fusion (CMF)” network to generate multistep features and achieve multiple interactions for two modalities, rather than focusing on modeling complex interactions between two modals like most current feature fusion methods. To avoid the linear increase of the computational cost, we share the parameters for each step in the CMF. Extensive experiments demonstrate that the proposed method can achieve competitive or better performance than the state of the art.
Frontal cortex function as derived from hierarchical predictive coding
(2018)

The frontal lobes are essential for human volition and goal-directed behavior, yet their function remains unclear. While various models have highlighted working memory, reinforcement learning, and cognitive control as key functions, a single framework for interpreting the range of effects observed in prefrontal cortex has yet to emerge. Here we show that a simple computational motif based on predictive coding can be stacked hierarchically to learn and perform arbitrarily complex goal-directed behavior. The resulting Hierarchical Error Representation (HER) model simulates a wide array of findings from fMRI, ERP, single-units, and neuropsychological studies of both lateral and medial prefrontal cortex. By reconceptualizing lateral prefrontal activity as anticipating prediction errors, the HER model provides a novel unifying account of prefrontal cortex function with broad implications for understanding the frontal cortex across multiple levels of description, from the level of single neurons to behavior.
Comparison of Collision Avoidance Systems and Applicability to Rail Transport
(2007)

The paper presents an overview of the state of the art in collision avoidance related with transportation systems like the automatic identification system (AIS) for maritime transportation, traffic alert and collision avoidance system/automatic dependent surveillance-broadcast (TCAS/ADS-B) for aircraft, and the car-2-car communication system (C2C) for road transportation. The examined systems rely on position detection and direct communication among vehicles. Alike a collision avoidance system for railway transportation "RCAS" is introduced. Focussing on the communication aspects, possible applicability of the examined state of the art systems to RCAS is studied. The analysis are performed at different communication system layers, namely application (APP) layer, media access control (MAC) layer and physical layer (PHY), which are the most relevant for a single hop network broadcast system as favorized in RCAS. Since multihop and addressed communication are not foreseen in a first RCAS approach, the network layer is not taken into account.
KYPO Cyber Range: Design and Use Cases
(2017)

The physical and cyber worlds are increasingly intertwined and exposed to cyber attacks. The KYPO cyber range provides complex cyber systems and networks in a virtualized, fully controlled and monitored environment. Time-efficient and cost-effective deployment is feasible using cloud resources instead of a dedicated hardware infrastructure. This paper describes the design decisions made during it’s development. We prepared a set of use cases to evaluate the proposed design decisions and to demonstrate the key features of the KYPO cyber range. It was especially cyber training sessions and exercises with hundreds of participants which provided invaluable feedback for KYPO platform development.
Information systems interoperability: What lies beneath?
(2004)

Interoperability is the most critical issue facing businesses that need to access information from multiple information systems. Our objective in this research is to develop a comprehensive framework and methodology to facilitate semantic interoperability among distributed and heterogeneous information systems. A comprehensive framework for managing various semantic conflicts is proposed. Our proposed framework provides a unified view of the underlying representational and reasoning formalism for the semantic mediation process. This framework is then used as a basis for automating the detection and resolution of semantic conflicts among heterogeneous information sources. We define several types of semantic mediators to achieve semantic interoperability. A domain-independent ontology is used to capture various semantic conflicts. A mediation-based query processing technique is developed to provide uniform and integrated access to the multiple heterogeneous databases. A usable prototype is implemented as a proof-of-concept for this work. Finally, the usefulness of our approach is evaluated using three cases in different application domains. Various heterogeneous datasets are used during the evaluation phase. The results of the evaluation suggest that correct identification and construction of both schema and ontology-schema mapping knowledge play very important roles in achieving interoperability at both the data and schema levels.
Toward Automatic Program Synthesis
(1971)

An elementary outline of the theorem-proving approach to automatic program synthesis is given, without dwelling on technical details. The method is illustrated by the automatic construction of both recursive and iterative programs operating on natural numbers, lists, and trees.
In order to construct a program satisfying certain specifications, a theorem induced by those specifications is proved, and the desired program is extracted from the proof. The same technique is applied to transform recursively defined functions into iterative programs, frequently with a major gain in efficiency.
It is emphasized that in order to construct a program with loops or with recursion, the principle of mathematical induction must be applied. The relation between the version of the induction rule used and the form of the program constructed is explored in some detail.
Developing a Feasible and Maintainable Ontology for Automatic Landscape Design
(2016)

In general, landscape architecture includes analysis, planning, design, administration and management of natural and artificial. An important aspect is the formation of socalled sustainable landscapes that allow maximum use of the environment, natural resources and promote sustainable restoration of ecosystems. For such purposes, a designer needs a complete database with existing and suitable plants, but no designing tool has one. Therefore it is presented the structure and the development of on ontology suitable for storing and managing all information and knowledge about plants. The advantage is that the format of the ontology allows the storage of any plant species (e.g. live or fossil) and automated reasoning. Ontology is a formal conceptualization of a particular knowledge about the world, through the explicit representation of basic concepts, relations, and inference rules about themselves. Therefore the ontology may be used by a design tool for helping the designer and choosing the best options for a sustainable landscape. Keywords—environment; landscapes; ontology; ontology-based simulation; sustainable landscapes
A novel approach to online social influence maximization
(2014)

Online social networks are becoming a true growth point of Internet. As individuals constantly desire to interact with each other, the ability for Internet to deliver this networking influence becomes much stronger. In this paper, we study the online social influence maximization problem, which is to find a small group of influential users that maximizes the spread of influence through networks. After a thorough analysis of existing models, especially two classical ones, namely Independent cascade and linear thresholds, we argue that their idea that each user can only be activated by its active neighbors is not applicable to online social networks, since in many applications there is no clear concept for the issue of "activation". In our proposed influence model, if there is a social influence path linking two nonadjacent individuals, the value of influence between these two individuals can be evaluated along the existing social path based on the influence transitivity property under certain constraints. To compute influence probabilities between two neighbors, we also develop a new method which leverages both structure of networks and historical data. With reasonably learned influence probabilities, we recast the problem of online influence maximization to a weighted maximum cut problem which analyzes the influence flow among graph vertices. By running a semidefinite program-based (GW) algorithm on a complete influence graph, an optimal seed set can be identified effectively. We also provide experimental results on real online social networks, showing that our algorithm significantly outperforms greedy methods.
Arabic Opinion Mining Using Parallel Decision Trees
(2017)

Opinion mining is an interested area of research, which epitomize the customer reviews of a product or service and express whether the opinions are positive or negative. Various methods have been proposed as classifiers for opinion mining such as Naïve Bayesian, and Support vector machine, these methods classify opinion without giving us the reasons about why the instance opinion is classified to certain class. Therefore, in our work, we investigate opinion mining of Arabic text at the document level, by applying decision trees classification classifier to have clear, understandable rule, also we apply parallel decision trees classifiers to have efficient results. We applied parallel decision trees on two Arabic corpus of text documents by using parallel implementation of RapidMiner tools. In case of applying parallel decision tree family on OCA we get the best results of accuracy (93.83%), f-measure (93.22) and consumed time 42 Sec at thread 4, one of the resulted rule is Urdu language lines. In case of applying parallel decision tree family on BHA we get the best results of accuracy (90.63%), f-measure (82.29) and consumed time 219 Sec at thread 4, one of the resulted rule is Urdu language lines.
Broadband Millimeter-Wave Power Combiner Using Compact SIW to Waveguide Transition
(2015)

A novel broadband millimeter-wave passive power combiner based on substrate integrated waveguide (SIW) to waveguide transition is presented in this letter. Two transition circuits are symmetrically inserted into the E-plane of the rectangular waveguide and function as power-combining network. To satisfy the wideband request of the power combiner, a broadband and compact SIW-to-waveguide transition circuit has been developed using antisymmetric tapered probe. A Ka-band four-way power combiner was fabricated and the measured results agree well with the simulated ones. The measured results show that the proposed combiner achieved a bandwidth of 52% from 23.5 to 40 GHz with better than 15 dB return loss and an insertion loss of 0.75 to 1.4 dB. This millimeter-wave power combiner can be employed in high power-combining system for its simple structure and broadband performance.
Security Viewpoint in a Reference Architecture Model for Cyber-Physical Production Systems
(2017)

Cyber-physical Production Systems (CPPS) are one of the technical driving forces behind the transformation of industrial production towards "digital factory of the future" in the context of Industry 4.0. Security is a major concern for such systems as they become more intelligent, interconnected, and coupled with physical devices. For various security activities from security analysis to designing security controls and architecture, a systematic and structured view and presentation of security-related information is required. Based on the draft standard of Reference Architecture Model for Industry 4.0 (RAMI 4.0), we propose a practical approach to establish a security viewpoint in the CPPS reference architecture model. We investigate the feasibility of using an architecture modeling tool to implement the concept and leverage existing work on models of layered architecture. We demonstrate the applicability for security analysis in two example case studies.
Dual Poisson-Disk Tiling: An Efficient Method for Distributing Features on Arbitrary Surfaces
(2008)

This paper introduces a novel surface-modeling method to stochastically distribute features on arbitrary topological surfaces. The generated distribution of features follows the Poisson disk distribution, so we can have a minimum separation guarantee between features and avoid feature overlap. With the proposed method, we not only can interactively adjust and edit features with the help of the proposed Poisson disk map, but can also efficiently re-distribute features on object surfaces. The underlying mechanism is our dual tiling scheme, known as the dual Poisson-disk tiling. First, we compute the dual of a given surface parameterization, and tile the dual surface by our specially-designed dual tiles; during the pre-processing, the Poisson disk distribution has been pre-generated on these tiles. By dual tiling, we can nicely avoid the problem of corner heterogeneity when tiling arbitrary parameterized surfaces, and can also reduce the tile set complexity. Furthermore, the dual tiling scheme is non-periodic, and we can also maintain a manageable tile set. To demonstrate the applicability of this technique, we explore a number of surface-modeling applications: pattern and shape distribution, bump-mapping, illustrative rendering, mold simulation, the modeling of separable features in texture and BTF, and the distribution of geometric textures in shell space.
Revisiting Distributed Synchronous SGD
(2017)

Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony. In contrast, the synchronous approach is often thought to be impractical due to idle time wasted on waiting for straggling workers. We revisit these conventional beliefs in this paper, and examine the weaknesses of both approaches. We demonstrate that a third approach, synchronous optimization with backup workers, can avoid asynchronous noise while mitigating for the worst stragglers. Our approach is empirically validated and shown to converge faster and to better test accuracies.